

'''simple docstring'''





import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetImgaImgPipeline,
    KandinskyVaaPriorEmbaEmbPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()


class  UpperCAmelCase (							UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					KandinskyVaaControlnetImgaImgPipeline
		SCREAMING_SNAKE_CASE_           =					['image_embeds', 'negative_image_embeds', 'image', 'hint']
		SCREAMING_SNAKE_CASE_           =					['image_embeds', 'negative_image_embeds', 'image', 'hint']
		SCREAMING_SNAKE_CASE_           =					[
		    'generator',
		    'height',
		    'width',
		    'strength',
		    'guidance_scale',
		    'num_inference_steps',
		    'return_dict',
		    'guidance_scale',
		    'num_images_per_prompt',
		    'output_type',
		    'return_dict',
		]
		SCREAMING_SNAKE_CASE_           =					False
		@property
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							return 32
		@property
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							return 32
		@property
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							return self.time_input_dim
		@property
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							return self.time_input_dim * 4
		@property
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							return 100
		@property
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							torch.manual_seed(0		)

							lowerCamelCase_  				=      {
							    'in_channels': 8,
							    # Out channels is double in channels because predicts mean and variance
							    'out_channels': 8,
							    'addition_embed_type': 'image_hint',
							    'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
							    'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
							    'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
							    'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
							    'layers_per_block': 1,
							    'encoder_hid_dim': self.text_embedder_hidden_size,
							    'encoder_hid_dim_type': 'image_proj',
							    'cross_attention_dim': self.cross_attention_dim,
							    'attention_head_dim': 4,
							    'resnet_time_scale_shift': 'scale_shift',
							    'class_embed_type': None,
							}

							lowerCamelCase_  				=      UNetaDConditionModel(**SCREAMING_SNAKE_CASE_		)
							return model
		@property
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							return {
							    "block_out_channels": [32, 32, 64, 64],
							    "down_block_types": [
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "DownEncoderBlock2D",
							        "AttnDownEncoderBlock2D",
							    ],
							    "in_channels": 3,
							    "latent_channels": 4,
							    "layers_per_block": 1,
							    "norm_num_groups": 8,
							    "norm_type": "spatial",
							    "num_vq_embeddings": 12,
							    "out_channels": 3,
							    "up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
							    "vq_embed_dim": 4,
							}
		@property
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							torch.manual_seed(0		)
							lowerCamelCase_  				=      VQModel(**self.dummy_movq_kwargs		)
							return model
		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							lowerCamelCase_  				=      self.dummy_unet
							lowerCamelCase_  				=      self.dummy_movq

							lowerCamelCase_  				=      {
							    'num_train_timesteps': 1000,
							    'beta_schedule': 'linear',
							    'beta_start': 0.00_085,
							    'beta_end': 0.012,
							    'clip_sample': False,
							    'set_alpha_to_one': False,
							    'steps_offset': 0,
							    'prediction_type': 'epsilon',
							    'thresholding': False,
							}

							lowerCamelCase_  				=      DDIMScheduler(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      {
							    'unet': unet,
							    'scheduler': scheduler,
							    'movq': movq,
							}

							return components
		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=0		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      floats_tensor((1, self.text_embedder_hidden_size)   ,			rng=random.Random(SCREAMING_SNAKE_CASE_		)		).to(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      floats_tensor((1, self.text_embedder_hidden_size)   ,			rng=random.Random(seed + 1		)		).to(
							    SCREAMING_SNAKE_CASE_		)
							# create init_image
							lowerCamelCase_  				=      floats_tensor((1, 3, 64, 64)   ,			rng=random.Random(SCREAMING_SNAKE_CASE_		)		).to(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      image.cpu().permute(0   ,			2   ,			3   ,			1		)[0]
							lowerCamelCase_  				=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_		)		).convert('RGB'		).resize((256, 256)		)
							# create hint
							lowerCamelCase_  				=      floats_tensor((1, 3, 64, 64)   ,			rng=random.Random(SCREAMING_SNAKE_CASE_		)		).to(SCREAMING_SNAKE_CASE_		)

							if str(SCREAMING_SNAKE_CASE_		).startswith('mps'		):
												lowerCamelCase_  				=      torch.manual_seed(SCREAMING_SNAKE_CASE_		)
							else:
												lowerCamelCase_  				=      torch.Generator(device=SCREAMING_SNAKE_CASE_		).manual_seed(SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      {
							    'image': init_image,
							    'image_embeds': image_embeds,
							    'negative_image_embeds': negative_image_embeds,
							    'hint': hint,
							    'generator': generator,
							    'height': 64,
							    'width': 64,
							    'num_inference_steps': 10,
							    'guidance_scale': 7.0,
							    'strength': 0.2,
							    'output_type': 'np',
							}
							return inputs






		def 			UpperCamelCase(					self		)  ->     Optional[Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      'cpu'

							lowerCamelCase_  				=      self.get_dummy_components()

							lowerCamelCase_  				=      self.pipeline_class(**SCREAMING_SNAKE_CASE_		)
							lowerCamelCase_  				=      pipe.to(SCREAMING_SNAKE_CASE_		)

							pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_		)		)
							lowerCamelCase_  				=      output.images

							lowerCamelCase_  				=      pipe(
							    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_		)   ,			return_dict=SCREAMING_SNAKE_CASE_   ,			)[0]

							lowerCamelCase_  				=      image[0, -3:, -3:, -1]
							lowerCamelCase_  				=      image_from_tuple[0, -3:, -3:, -1]

							assert image.shape == (1, 64, 64, 3)

							lowerCamelCase_  				=      np.array(
							    [0.54_985_034, 0.55_509_365, 0.52_561_504, 0.5_570_494, 0.5_593_818, 0.5_263_979, 0.50_285_643, 0.5_069_846, 0.51_196_736]		)
							assert (
							    np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
							), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
							assert (
							    np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2
							), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''


@slow
@require_torch_gpu
class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def 			UpperCamelCase(					self		)  ->     Tuple:






							'''simple docstring'''



							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()






		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/kandinskyv22_controlnet_img2img_robotcat_fp16.npy'		)

							lowerCamelCase_  				=      load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'		)
							lowerCamelCase_  				=      init_image.resize((512, 512)		)

							lowerCamelCase_  				=      load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/kandinskyv22/hint_image_cat.png'		)
							lowerCamelCase_  				=      torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_		)		).float() / 255.0
							lowerCamelCase_  				=      hint.permute(2   ,			0   ,			1		).unsqueeze(0		)

							lowerCamelCase_  				=      'A robot, 4k photo'

							lowerCamelCase_  				=      KandinskyVaaPriorEmbaEmbPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-prior'   ,			torch_dtype=torch.floataa		)
							pipe_prior.to(SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      KandinskyVaaControlnetImgaImgPipeline.from_pretrained(
							    'kandinsky-community/kandinsky-2-2-controlnet-depth'   ,			torch_dtype=torch.floataa		)
							lowerCamelCase_  				=      pipeline.to(SCREAMING_SNAKE_CASE_		)

							pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      torch.Generator(device='cpu'		).manual_seed(0		)

							lowerCamelCase_				,lowerCamelCase_  				=      pipe_prior(
							    SCREAMING_SNAKE_CASE_   ,			image=SCREAMING_SNAKE_CASE_   ,			strength=0.85   ,			generator=SCREAMING_SNAKE_CASE_   ,			negative_prompt=''   ,			).to_tuple()

							lowerCamelCase_  				=      pipeline(
							    image=SCREAMING_SNAKE_CASE_   ,			image_embeds=SCREAMING_SNAKE_CASE_   ,			negative_image_embeds=SCREAMING_SNAKE_CASE_   ,			hint=SCREAMING_SNAKE_CASE_   ,			generator=SCREAMING_SNAKE_CASE_   ,			num_inference_steps=100   ,			height=512   ,			width=512   ,			strength=0.5   ,			output_type='np'   ,			)

							lowerCamelCase_  				=      output.images[0]

							assert image.shape == (512, 512, 3)

							assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)





"""simple docstring"""



import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    StableDiffusionSAGPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Dict			     =					StableDiffusionSAGPipeline
    a   :					Any			     =					TEXT_TO_IMAGE_PARAMS
    a   :					Optional[Any]			     =					TEXT_TO_IMAGE_BATCH_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Optional[int]			     =					TEXT_TO_IMAGE_IMAGE_PARAMS
    a   :					Tuple			     =					False







    def   _snake_case       (      self      :							List[Any]  )		->Tuple:






        '''simple docstring'''






        torch.manual_seed(0  )
        _UpperCAmelCase        = UNetaDConditionModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       sample_size=32  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")  ,       cross_attention_dim=32  ,       )
        _UpperCAmelCase        = DDIMScheduler(
            beta_start=0.0_0_0_8_5  ,       beta_end=0.0_1_2  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__UpperCamelCase  ,       set_alpha_to_one=__UpperCamelCase  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = AutoencoderKL(
            block_out_channels=[32, 64]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]  ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]  ,       latent_channels=4  ,       )
        torch.manual_seed(0  )
        _UpperCAmelCase        = CLIPTextConfig(
            bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=32  ,       intermediate_size=37  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=10_00  ,       )
        _UpperCAmelCase        = CLIPTextModel(__UpperCamelCase  )
        _UpperCAmelCase        = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  )

        _UpperCAmelCase        = {
            """unet""": unet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components







    def   _snake_case       (      self      :							Union[str, Any]  ,       __UpperCamelCase      :							Union[str, Any]  ,       __UpperCamelCase      :							Any=0  )		->Any:






        '''simple docstring'''






        if str(__UpperCamelCase  ).startswith("""mps"""  ):
            _UpperCAmelCase        = torch.manual_seed(__UpperCamelCase  )
        else:
            _UpperCAmelCase        = torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
        _UpperCAmelCase        = {
            """prompt""": """.""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 1.0,
            """sag_scale""": 1.0,
            """output_type""": """numpy""",
        }
        return inputs







    def   _snake_case       (      self      :							Optional[Any]  )		->Optional[int]:






        '''simple docstring'''






        super().test_inference_batch_single_identical(expected_max_diff=3e-3  )







@slow
@require_torch_gpu
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->str:






        '''simple docstring'''






        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()







    def   _snake_case       (      self      :							Tuple  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""CompVis/stable-diffusion-v1-4"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.1_5_6_8, 0.1_7_3_8, 0.1_6_9_5, 0.1_6_9_3, 0.1_5_0_7, 0.1_7_0_5, 0.1_5_4_7, 0.1_7_5_1, 0.1_9_4_9]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							Union[str, Any]  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  )

        _UpperCAmelCase        = output.images

        _UpperCAmelCase        = image[0, -3:, -3:, -1]

        assert image.shape == (1, 5_12, 5_12, 3)
        _UpperCAmelCase        = np.array([0.3_4_5_9, 0.2_8_7_6, 0.2_5_3_7, 0.3_0_0_2, 0.2_6_7_1, 0.2_1_6_0, 0.3_0_2_6, 0.2_2_6_2, 0.2_3_7_1]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-2







    def   _snake_case       (      self      :							str  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = StableDiffusionSAGPipeline.from_pretrained("""stabilityai/stable-diffusion-2-1-base"""  )
        _UpperCAmelCase        = sag_pipe.to(__UpperCamelCase  )
        sag_pipe.set_progress_bar_config(disable=__UpperCamelCase  )

        _UpperCAmelCase        = """."""
        _UpperCAmelCase        = torch.manual_seed(0  )
        _UpperCAmelCase        = sag_pipe(
            [prompt]  ,       width=7_68  ,       height=5_12  ,       generator=__UpperCamelCase  ,       guidance_scale=7.5  ,       sag_scale=1.0  ,       num_inference_steps=20  ,       output_type="""np"""  ,       )

        _UpperCAmelCase        = output.images

        assert image.shape == (1, 5_12, 7_68, 3)